Применение модели освоения языка к решению задачи обработки малых языков

The problem of building a computer model of a small language was under solution. The relevance of this task is due to the following considerations: the need to eliminate the information inequality between speakers of different languages; the need for new tools for the study of poorly understood languages, as well as innovative approaches to language modeling in the low-resource context; the problem of supporting and developing small languages.There are three main objectives in solving the problem of small natural language processing at the stage of describing the problem situation: to justify the problem of modeling language in the context of resource scarcity as a special task in the field of natural languages processing, to review the literature on the relevant topic, to develop the concept of language acquisition model with a relatively small number of available resources. Computer modeling techniques using neural networks, semi-supervised learning and reinforcement learning were involved.The paper provides a review of the literature on modeling the learning of vocabulary, morphology, and grammar of a child's native language. Based on the current understanding of the language acquisition and existing computer models of this process, the architecture of the system of small language processing, which is taught through modeling of ontogenesis, is proposed. The main components of the system and the principles of their interaction are highlighted. The system is based on a module built on the basis of modern dialogical language models and taught in some rich-resources language (e.g., English). During training, an intermediate layer is used which represents statements in some abstract form, for example, in the symbols of formal semantics. The relationship between the formal recording of utterances and their translation into the target low-resource language is learned by modeling the child's acquisition of vocabulary and grammar of the language. One of components stands for the non-linguistic context in which language learning takes place.This article explores the problem of modeling small languages. A detailed substantiation of the relevance of modeling small languages is given: the social significance of the problem is noted, the benefits for linguistics, ethnography, ethnology and cultural anthropology are shown. The ineffectiveness of approaches applied to large languages in conditions of a lack of resources is noted. A model of language learning by means of ontogenesis simulation is proposed, which is based both on the results obtained in the field of computer modeling and on the data of psycholinguistics.Решается задача построения компьютерной модели малого языка. Ее актуальность обусловлена необходимостью устранения информационного неравенства между носителями различных языков, востребованностью новых инструментов для исследования малоизученных языков и инновационных подходов к моделированию языка в условиях дефицита ресурсов, необходимостью поддержки и развития языков малых народов.При решении задачи обработки малых языков на этапе описания проблемной ситуации преследуются три основные цели: обоснование проблемы моделирования языка в условиях дефицита ресурсов как особой задачи в сфере моделирования естественных языков, обзор литературы по соответствующей теме и разработка концепции модели усвоения языка с относительно малым числом доступных ресурсов. Используются методы компьютерного моделирования с применением нейронных сетей, обучение с частичным привлечением учителя и обучение с подкреплением.В  работе  приведен обзор  литературы, посвященной моделированию  изучения  лексики,  морфологии и грамматики родного языка ребенком. На основании современных представлений о ходе изучения языка предложена архитектура системы обработки малого языка, которая при обучении опирается на компьютерное моделирование онтогенеза. Выделены основные компоненты системы и принципы их взаимодействия. В основе системы лежит модуль, построенный на базе современных диалоговых языковых моделей  и  обученный на  каком-либо крупном языке,  например английском. При обучении используется промежуточный слой, который представляет высказывания в некотором абстрактном виде, например, в символах формальной семантики. Соотношение между формальной записью высказываний и их переводом на целевой малый язык изучается методом моделирования процесса усвоения лексики и грамматики языка ребенком. Отдельный компонент имитирует неязыковой контекст, в котором происходит изучение языка.В статье исследуется задача моделирования малых языков. Дано подробное обоснование актуальности моделирования малых языков: показана социальная значимость этой проблемы, польза ее решения для лингвистики, этнографии, этнологии и культурной антропологии. Отмечена неэффективность подходов, применяемых к крупным языкам, в условиях дефицита ресурсов. Предложена модель изучения языка с помощью имитации онтогенеза, которая опирается как на полученные результаты в области компьютерного моделирования, так и на данные психолингвистики

Introducing Meta-analysis in the Evaluation of Computational Models of Infant Language Development

Computational models of child language development can help us understand the cognitive underpinnings of the language learning process, which occurs along several linguistic levels at once (e.g., prosodic and phonological). However, in light of the replication crisis, modelers face the challenge of selecting representative and consolidated infant data. Thus, it is desirable to have evaluation methodologies that could account for robust empirical reference data, across multiple infant capabilities. Moreover, there is a need for practices that can compare developmental trajectories of infants to those of models as a function of language experience and development. The present study aims to take concrete steps to address these needs by introducing the concept of comparing models with large-scale cumulative empirical data from infants, as quantified by meta-analyses conducted across a large number of individual behavioral studies. We formalize the connection between measurable model and human behavior, and then present a conceptual framework for meta-analytic evaluation of computational models. We exemplify the meta-analytic model evaluation approach with two modeling experiments on infant-directed speech preference and native/non-native vowel discrimination.Peer reviewe

TOWARDS THE GROUNDING OF ABSTRACT CATEGORIES IN COGNITIVE ROBOTS

The grounding of language in humanoid robots is a fundamental problem, especially in social scenarios which involve the interaction of robots with human beings. Indeed, natural language represents the most natural interface for humans to interact and exchange information about concrete entities like KNIFE, HAMMER and abstract concepts such as MAKE, USE. This research domain is very important not only for the advances that it can produce in the design of human-robot communication systems, but also for the implication that it can have on cognitive science. Abstract words are used in daily conversations among people to describe events and situations that occur in the environment. Many scholars have suggested that the distinction between concrete and abstract words is a continuum according to which all entities can be varied in their level of abstractness. The work presented herein aimed to ground abstract concepts, similarly to concrete ones, in perception and action systems. This permitted to investigate how different behavioural and cognitive capabilities can be integrated in a humanoid robot in order to bootstrap the development of higher-order skills such as the acquisition of abstract words. To this end, three neuro-robotics models were implemented. The first neuro-robotics experiment consisted in training a humanoid robot to perform a set of motor primitives (e.g. PUSH, PULL, etc.) that hierarchically combined led to the acquisition of higher-order words (e.g. ACCEPT, REJECT). The implementation of this model, based on a feed-forward artificial neural networks, permitted the assessment of the training methodology adopted for the grounding of language in humanoid robots. In the second experiment, the architecture used for carrying out the first study was reimplemented employing recurrent artificial neural networks that enabled the temporal specification of the action primitives to be executed by the robot. This permitted to increase the combinations of actions that can be taught to the robot for the generation of more complex movements. For the third experiment, a model based on recurrent neural networks that integrated multi-modal inputs (i.e. language, vision and proprioception) was implemented for the grounding of abstract action words (e.g. USE, MAKE). Abstract representations of actions ("one-hot" encoding) used in the other two experiments, were replaced with the joints values recorded from the iCub robot sensors. Experimental results showed that motor primitives have different activation patterns according to the action's sequence in which they are embedded. Furthermore, the performed simulations suggested that the acquisition of concepts related to abstract action words requires the reactivation of similar internal representations activated during the acquisition of the basic concepts, directly grounded in perceptual and sensorimotor knowledge, contained in the hierarchical structure of the words used to ground the abstract action words.This study was financed by the EU project RobotDoC (235065) from the Seventh Framework Programme (FP7), Marie Curie Actions Initial Training Network

La estructuración temática en inglés y español: anotación contrastiva de un corpus bilingüe para aplicaciones lingüísticas y computacionales

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Filología, Departamento de Filología Inglesa, leída el 04-12-2015Thematization is recognized as a fundamental phenomenon in the construction of messages and texts by di erent linguistic schools. This location within a text privileges the elements that guide the reader in the orientation and interpretation of discourse at di erent levels. Thematizing a linguistic unit by locating it in the rst-initial position of a clause, paragraph, or text, confers upon it a special status: a signal of the organizational strategy which characterizes di erent text types playing a role as a variable in the distinction of registers, text types and genres. However, in spite of the importance of the study of thematization for message and textual structuring, to date there are no linguistic studies that have undertook the task of validating its aspects in a comparative manner, either for linguistic or computational purposes. This study, therefore, lls a research gap by implementing a methodology based on contrastive corpus annotation, which allows to empirically validate aspects of the phenomenon of Thematization in English and Spanish, it also seeks to develop a bilingual English-Spanish comparable corpus of newspaper texts automatically annotated with thematic features at clausal and discourse levels. The empirically validated categories (Thematic Field and its elements: Textual Theme, Interpersonal Theme, PreHead and Head) are used to annotate a larger corpus of three newspaper genres news reports, editorials and letters to the editor in terms of thematic choices. This characterization, reveals interesting results, such as the use of genre-speci c strategies in thematic position. In addition, the thesis investigates the possibility to automate the annotation of thematic features in the bilingual corpus through the development of a set of JAVA rules implemented in GATE. It also shows the e cacy of this method in comparison with the manual annotation results...Depto. de Estudios Ingleses: Lingüística y LiteraturaFac. de FilologíaTRUEunpu